DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
The instant application is a National Stage Application under 35 U.S.C. 371 of co-pending PCT application PCT/US21/59829 filed 18 November 2021, which claims the benefit of U.S. Provisional Application No. 63/115,768 filed 19 November 2020. Benefit is acknowledged. At this point in examination, the effective filing date of claims 1-20 is 19 November 2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 23 June 2023 and 11 November 2025 comply with 37 CFR 1.98. Accordingly, all references listed have been considered by the examiner. It is noted that a duplicate copy of the IDS submitted on 23 June 2023 was received, which has been crossed out as all references listed were previously considered.
Drawings
The drawings filed on 17 May 2023 have been received and are accepted.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Specifically, para. [00086] contains two hyperlinks. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 9 recites a biological testing system comprising a processor configured with a set of computer instructions operable, when executed, to cause the system to perform a method comprising: (a) creating a plurality of test mixtures in a plurality of test wells; (b) incubating each of the test mixtures; and (c) capturing an image of each test mixture. Similarly, claim 15 recites a computer program product comprising a non-transitory computer readable medium having stored thereon a set of computer instructions operable, when executed, to cause a biological testing system to perform a method comprising: (a) creating a plurality of test mixtures in a plurality of test wells; (b) incubating each of the test mixtures; and (c) capturing an image of each test mixture. For both of these claims, there is insufficient structure provided for performing steps (a)-(c) to make clear the scope and meaning of the claim.
It is unclear how a system comprising a processor configured with computer instructions is able to create/incubate/image test mixtures without additional equipment such as a test well or an imaging device. Similarly, it is unclear how a computer program product could cause a system to create/incubate/image test mixtures without the system comprising equipment to perform such functions. While the written description requirement may be satisfied through disclosure of function and minimal structure when there is a well-established correlation between structure and function, there is no such correlation in the field of microbiology that would allow a skilled artisan to understand the inventor to be in possession of the claimed invention at the time of filing. Therefore, there is not sufficient detail that one skilled in the art could reasonably conclude that the inventor had possession of the claimed invention as of the filing date, and the claims are rejected for failing to comply with the written description requirement. Claims 10-14 and 16-20 are also rejected due to their dependency on claims 9 and 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract ideas) without significantly more. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims 1-20 describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis. See MPEP § 2106.03. Claims 1-8 are properly to one of the four statutory categories because the claimed invention is a method, which falls into the process category [Step 1: Yes]. Claims 9-14 are properly to one of the four statutory categories because the claimed invention is a system, which falls into the machine category [Step 1: Yes]. Claims 15-20 are properly to one of the four statutory categories because the claimed invention is a computer program product comprising a non-transitory computer readable medium having stored thereon a set of computer instructions, which falls into the manufacture category [Step 1: Yes].
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations. See MPEP § 2106.04(a)(2)(I). Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions. See MPEP § 2106.04(a)(2)(III). Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships. See MPEP § 2106.04(a)(2)(II). Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature. See MPEP § 2106.04(b)-(c).
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-20 recite abstract ideas that fall into the groupings of mathematical concepts and mental processes.
Independent claims 1, 9, and 15 recite the following limitations, which describe abstract ideas:
(d) obtaining a plurality of growth predictions by performing steps comprising, for each test mixture from the plurality of test mixtures, providing a data sequence to a machine learning model, wherein: (i) the data sequence comprises a plurality of input items; (ii) each input item from the plurality of input items corresponds to an imaging time from the plurality of imaging times; and (iii) the machine learning model is adapted to recognize, and to make growth predictions based on, temporal sequences; and
(e) generating a minimum inhibitory concentration, MIC, determination for the biological sample based on the plurality of growth predictions.
The limitation of obtaining a plurality of growth predictions involves using algorithms for processing time series data, which constitutes a mathematical concept, and analyzing growth patterns over time, which constitutes a mental process. The limitation of generating a MIC involves comparing predictions to identify a threshold/concentration, which constitutes a mathematical concept, and evaluating experimental results to determine effectiveness, which constitutes a mental process.
Dependent claims 2-4, 7-8, 10-12, and 16-18 recite the following limitations, which narrow or describe abstract ideas:
Claims 2, 10, and 16 recite (b) obtaining the plurality of growth predictions comprises, for each test mixture from the plurality of test mixtures: (i) providing a temporal sequence based on the data sequence for that test mixture to each recurrent neural network from the plurality of recurrent neural networks; (ii) obtaining, from each recurrent neural network from the plurality of recurrent neural networks, an intermediate growth prediction for that test mixture; and (iii) obtaining, from the dense layer, a growth prediction for that test mixture based on the intermediate growth predictions for that test mixture obtained from the recurrent neural networks.
Claims 3, 11, and 17 recite (b) the dense layer is adapted to, for each test mixture, weight the intermediate growth predictions for that test mixture from the plurality of recurrent neural networks based on the identification of the microorganism.
Claims 4, 12, and 18 recite wherein the machine learning model is adapted to, for each test mixture from the plurality of test mixtures, generate the temporal sequence based on the data sequence for that test mixture by performing steps comprising: (a) for each imaging time from the plurality of imaging times, obtaining a doubling value by applying a log base 2 transformation to the input item corresponding to that imaging time comprised by the data sequence for that test mixture; and (b) for each doubling value except the doubling value corresponding to a first imaging time, obtaining a doubling value change by subtracting a doubling value corresponding to a preceding imaging time.
Claim 7 recites (b) the plurality of growth predictions comprises, for each test mixture from the plurality of test mixtures, a growth prediction corresponding to that test mixture; and (c) generating the MIC determination for the biological sample based on the plurality of growth predictions comprises determining that a lowest concentration corresponding to a test mixture with a corresponding growth prediction of no growth as the MIC determination.
Claim 8 recites wherein the plurality of test mixtures comprises a growth mixture, in which the corresponding antimicrobial concentration is no antimicrobial.
The limitation of claims 2, 10, and 16 describes the data flow through machine learning layers for time-series processing and predictions, which constitutes an abstract idea within the mathematical concepts grouping. The limitation of claims 3, 11, and 17 involves feature-based weighing and conditional computation in the model, which constitutes an abstract idea within the mathematical concepts grouping. The limitations of claims 4, 12, and 18 recite the mathematical transformations performed by the model, which constitutes an abstract idea within the mathematical concepts grouping. Limitation (b) of claim 7 narrows the abstract idea of claim 1 by specifying that the growth predictions comprise a growth prediction for each test mixture. Limitation (c) of claim 7 involved threshold comparison and selection logic, which constitutes an abstract idea within the mathematical concepts and mental processes groupings. The limitation of claim 8 narrows the abstract idea of claim 7 by specifying a growth mixture with no antimicrobial as a control to support the mathematical comparison.
Claims 5-6, 13-14, and 19-20 do not narrow or describe judicial exceptions, but inherit the exceptions of the claims upon which they depend. Therefore, claims 1-20 recite abstract ideas – namely mathematical concepts and mental processes [Step 2A, Prong One: Yes].
Prong Two
Claims 1-20 as a whole do not integrate the recited judicial exception into a practical application. A claim that recites a judicial exception [Prong One] is deemed to be directed to a judicial exception [Step 2A] unless the claim as a whole contains additional elements that integrate the exception into a practical application [Prong Two]. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP §§ 2106.04(d) and 2106.05(e). A claim does not integrate a judicial exception into a practical application by reciting insignificant extra-solution activity, generally linking the exception to a particular technological environment or field of use, merely reciting to apply the exception, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP § 2106.04(d)(I). Insignificant extra-solution activities are nominal or tangential additions to a claim that are incidental to the primary process or product, including both pre-solution and post-solution activity (e.g. pre-solution data gathering for use in a process). If integrated into a practical application, the claim is eligible; otherwise, it is directed to the judicial exception, necessitating further analysis at Step 2B.
Independent claims 1, 9, and 15 recite the following limitations, which are additional elements:
(a) creating a plurality of test mixtures in a plurality of test wells, wherein: (i) each test mixture from the plurality of test mixtures is inoculated using a biological sample; (ii) each test mixture from the plurality of test mixtures comprises an antimicrobial solution comprising an antimicrobial agent; (iii) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that test mixture differs from the antimicrobial solution in each other test mixture from the plurality of test mixtures; and (iv) the same biological sample is used to inoculate each test mixture from the plurality of test mixtures;
(b) incubating each of the test mixtures;
(c) for each test mixture from the plurality of test mixtures, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, capturing an image of that test mixture;
These limitations describe data gathering/preparation, which constitutes insignificant extra-solution activities that generally link the use of the judicial exceptions to a particular technological environment or field of use and do not integrate the exceptions into a practical application. See MPEP §§ 2106.05(g)-(h).
Dependent claims 2-3, 5-7, 10-11, 13-14, 16-17, and 19-20 recite the following limitations, which are additional elements:
Claims 2, 10, and 16 recite (a) the machine learning model comprises: (i) a network cluster comprising a plurality of recurrent neural networks; and (ii) a dense layer comprising a feed forward neural network;
Claims 3, 11, and 17 recite (a) the machine learning model is adapted to receive an identification of a microorganism corresponding to the biological sample
Claims 5, 13, and 19 recite wherein the plurality of recurrent neural network comprises 16 recurrent neural networks.
Claims 6, 14, and 20 recite wherein the plurality of recurrent neural networks comprises 24 gated recurrent units.
Claim 7 recites (a) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that text mixture has a corresponding antimicrobial concentration which is different from the antimicrobial concentrations which correspond to the other test mixtures from the plurality of test mixtures
The limitation of claims 2, 10, and 16 describes the architecture of the neural network, which amounts to applying the abstract ideas on a computer/generic machine learning components in the field of microbiology, which does not integrate the judicial exceptions into a practical application. This limitation is narrowed by the limitations of claims 5-6, 13-14, and 19-20 by specifying the count of networks/units in the machine learning architecture, which is an arbitrary hyperparameter choice that does not integrate the judicial exceptions into a practical application. The limitation of claims 3, 11, and 17 further narrows the neural network by specifying that it is adapted to receive an identification of a microorganism, which does not integrate the judicial exceptions into a practical application when the neural network is merely an avenue to apply the exceptions. The limitation of claim 7 specifies that the antimicrobial solutions in the test mixtures have different concentrations, which merely narrows the data gathering/preparation limitations of claim 1 and does not integrate the judicial exceptions into a practical application. Finally, claims 4, 8, 12, and 18 do not include any additional elements.
The claims as a whole merely recite insignificant extra-solution activities and abstract ideas implemented on generic computer components without meaningful limitations that tie it to a specific technological improvement. Therefore, claims 1-20 do not contain additional elements that integrate the recited abstract ideas into a practical application [Step 2A, Prong Two: No].
Step 2B
Claims 1-20 do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception itself. Under Step 2B, the claim is analyzed to determine whether there are any additional elements that, individually or in combination, constitute an “inventive concept" sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. See MPEP § 2106.05; and Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014).
Independent claims 1, 9, and 15 recite the following limitations, which are additional elements:
(a) creating a plurality of test mixtures in a plurality of test wells, wherein: (i) each test mixture from the plurality of test mixtures is inoculated using a biological sample; (ii) each test mixture from the plurality of test mixtures comprises an antimicrobial solution comprising an antimicrobial agent; (iii) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that test mixture differs from the antimicrobial solution in each other test mixture from the plurality of test mixtures; and (iv) the same biological sample is used to inoculate each test mixture from the plurality of test mixtures;
(b) incubating each of the test mixtures;
(c) for each test mixture from the plurality of test mixtures, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, capturing an image of that test mixture;
Dependent claims 2-3, 5-7, 10-11, 13-14, 16-17, and 19-20 recite the following limitations, which are additional elements:
Claims 2, 10, and 16 recite (a) the machine learning model comprises: (i) a network cluster comprising a plurality of recurrent neural networks; and (ii) a dense layer comprising a feed forward neural network;
Claims 3, 11, and 17 recite (a) the machine learning model is adapted to receive an identification of a microorganism corresponding to the biological sample
Claims 5, 13, and 19 recite wherein the plurality of recurrent neural network comprises 16 recurrent neural networks.
Claims 6, 14, and 20 recite wherein the plurality of recurrent neural networks comprises 24 gated recurrent units.
Claim 7 recites (a) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that text mixture has a corresponding antimicrobial concentration which is different from the antimicrobial concentrations which correspond to the other test mixtures from the plurality of test mixtures
The independent claim limitations describe conventional data gathering/preparation steps in antimicrobial susceptibility testing, which constitutes insignificant extra-solution activities that generally link the use of the judicial exceptions to a particular technological environment or field of use and do not add significantly more than the exceptions. See MPEP §§ 2106.05(g)-(h); and Anand Srinivasan et al., High-throughput microarray for antimicrobial susceptibility testing, 16 Biotechnol Rep. 44-47, 44 col.2 para.2 – 45 col.2 para.2 (8 November 2017). Similarly, the limitation of claim 7 describes conventional data gathering/preparation steps in antimicrobial susceptibility testing. See Srinivasan, at 44 col.1 para.1.
The limitation of claims 2, 10, and 16 describes a conventional architecture of the neural network, which amounts to applying the abstract ideas on a computer/generic machine learning components in the field of microbiology, which does not add significantly more than the exceptions. See Jeff Donahue et al., Long-term Recurrent Convolutional Networks for Visual Recognition and Description, arXiv:1411.4389v4 Figs.1-2 (31 May 2016). This limitation is narrowed by the limitations of claims 5-6, 13-14, and 19-20 by specifying the count of networks/units in the machine learning architecture, which is an arbitrary hyperparameter choice that does not add significantly more. The limitation of claims 3, 11, and 17 further narrows the neural network by specifying that it is adapted to receive an identification of a microorganism, which does not add significantly more when the neural network is merely a conventional avenue to apply the exceptions. See Jana Wäldchen & Patrick Mäder, Machine learning for image based species identification, 9(11) Methods Ecol Evol 2216–2225, abstract (13 August 2018).
Overall, claims 1-20 amount to no more than insignificant extra-solution activities and implementing the abstract ideas on conventional computers in a routine way. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself because the claims recite additional elements that equate to insignificant extra-solution activity and mere instructions to apply the recited abstract ideas in a generic way or in a generic computing environment. Therefore, claims 1-20 are rejected for failing to set forth patent eligible subject matter under 35 U.S.C. 101 because the claimed invention recites abstract ideas [Step 2A, Prong One: Yes] and the additional elements do not integrate the judicial exception into a practical application [Step 2A, Prong Two: No] and do not amount to claiming significantly more than the recited exception [Step 2B: No].
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 7-9, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hui Yu et al., Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy, 90(10) Anal. Chem. 6314-22 (20 April 2018) (hereinafter “Yu”), as evidenced by C3.ai., Infrastructure: Machine Learning Hardware Requirements (15 May 2021) (hereinafter “C3.ai.”).
Regarding independent claims 1, 9, and 15, Yu discloses an antimicrobial susceptibility testing (AST) method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. At abstract. Yu focuses on Escherichia coli, a bacterial pathogen that is the most common cause of urinary tract infections (UTI), and five relevant antibiotics for treating UTI: polymyxin B (PMB), streptomycin, ciprofloxacin, aztreonam, and ampicillin. At 6315 col.1 para.2. Yu prepares mixtures of E. coli and one of the antibiotics in a microfluidic chip with six parallel detection channels, allowing AST detection with different concentration of antibiotics simultaneously. At 6316 col.1 paras.1-2 ((a) creating a plurality of test mixtures in a plurality of test wells, wherein: (i) each test mixture from the plurality of test mixtures is inoculated using a biological sample; (ii) each test mixture from the plurality of test mixtures comprises an antimicrobial solution comprising an antimicrobial agent; (iii) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that test mixture differs from the antimicrobial solution in each other test mixture from the plurality of test mixtures; and (iv) the same biological sample is used to inoculate each test mixture from the plurality of test mixtures).
Yu records videos of the microfluidic chip at 100 frames per second (fps) immediately (0 min) and after every 30 minutes, with each video lasting for 30 seconds. At 6316 col.1 para.2 ((b) incubating each of the test mixtures; (c) for each test mixture from the plurality of test mixtures, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, capturing an image of that test mixture). The videos of the bacterial cells at each time of recording are compressed into images as the input data for the deep learning model. At 6317 col.2 para.2 (for each test mixture from the plurality of test mixtures, providing a data sequence to a machine learning model, wherein: (i) the data sequence comprises a plurality of input items; (ii) each input item from the plurality of input items corresponds to an imaging time from the plurality of imaging times). For each input image, the model determines the total number of uninhibited bacterial cells over time for each antibiotic concentration and produces inhibition curves. At 6317 col.2 para.3 (((d) obtaining a plurality of growth predictions by performing steps comprising; (iii) the machine learning model is adapted to recognize, and to make growth predictions based on, temporal sequences). From the inhibition curves, Yu determines the minimum inhibitory concentration (MIC), which describes the lowest concentration of an antibiotic that inhibits the bacterial strain. At 6317 col.2 para.3 ((e) generating a minimum inhibitory concentration, MIC, determination for the biological sample based on the plurality of growth predictions).
Yu does not explicitly disclose a processor configured with a set of computer instructions or a computer program product comprising a non-transitory computer readable medium having stored thereon a set of computer instructions. However, Yu discloses that all computations were performed with a desktop computer, which inherently requires a processor and a set of computer instructions. At 6316 col.2 para.1. Additionally, Yu discloses a deep learning model, which necessarily involves a non-transitory computer readable medium with instructions stored thereon to be executed by a processor. See C3.ai., §§ Processors:CPUs, GPUs, TPUs, and FPGAs – Memory and Storage.
Regarding claim 7, Yu prepares mixtures of E. coli and one of the antibiotics in a microfluidic chip with six parallel detection channels, allowing AST detection with different concentration of antibiotics simultaneously. At 6316 col.1 paras.1-2 (the method of claim 1, wherein: (a) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that text mixture has a corresponding antimicrobial concentration which is different from the antimicrobial concentrations which correspond to the other test mixtures from the plurality of test mixtures). Yu discloses that the deep learning model plots an inhibition curve for each antibiotic concentration. At 6319 col.1 para.2 ((b) the plurality of growth predictions comprises, for each test mixture from the plurality of test mixtures, a growth prediction corresponding to that test mixture). Yu determines the MIC as the minimal concentration that inhibits the increase of total bacterial number. At 6319 col.1 para.3 ((c) generating the MIC determination for the biological sample based on the plurality of growth predictions comprises determining that a lowest concentration corresponding to a test mixture with a corresponding growth prediction of no growth as the MIC determination).
Regarding claim 8, Yu discloses preparing a test mixture with only broth as a control group, and training the deep learning model with uninhibited/control cell images. At 6316 col.1 para.3 – col.2 para.2 (the method of claim 7,wherein the plurality of test mixtures comprises a growth mixture, in which the corresponding antimicrobial concentration is no antimicrobial).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Zahra Karevan & Johan A. K. Suykens, Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting, arXiv:1811.06341 (15 November 2018) (hereinafter “Karevan”), as evidenced by Jeremy Howard, Dense vs convolutional vs fully connected layers (November 2016) (hereinafter “Howard”).
Regarding claims 2, 10, and 16, Yu discloses the steps of independent claims 1, 9, and 15 (see 102 rejection above). Yu teaches that the deep learning model is a feed forward convolutional neural network (CNN) with a fully connected layer. At 6317 col.2 para.3; Figure S3. A fully connected layer is the same as a dense layer, as evidenced by Howard. At para.1 (wherein: (a) the machine learning model comprises: (ii) a dense layer comprising a feed forward neural network). Yu discloses that the deep learning model plots an inhibition curve for each antibiotic concentration. At 6319 col.1 para.2 ((b) obtaining the plurality of growth predictions comprises, for each test mixture from the plurality of test mixtures). Yu fails to disclose a network cluster comprising a plurality of recurrent neural networks, and obtaining the plurality of growth predictions comprises: (i) providing a temporal sequence based on the data sequence for that test mixture to each recurrent neural network from the plurality of recurrent neural networks; (ii) obtaining, from each recurrent neural network from the plurality of recurrent neural networks, an intermediate growth prediction for that test mixture; and (iii) obtaining, from the dense layer, a growth prediction for that test mixture based on the intermediate growth predictions for that test mixture obtained from the recurrent neural networks.
However, Karevan discloses a spatio-temporal stacked Long Short-Term Memory (LSTM) model, which are a type of recurrent neural network (RNN). At 1 paras.2-3. Karevan discloses using a 2-layer stacked LSTM model with 5 LSTM models in the first layer where each LSTM model corresponds to a location where data was obtained. At 2 para.2 (a network cluster comprising a plurality of recurrent neural networks). Karevan teaches inputting spatio-temporal data to each LSTM model to produce a hidden state at each time in the sequence. At 1 para.4 – 2 para.2 ((i) providing a temporal sequence based on the data sequence for that test mixture to each recurrent neural network from the plurality of recurrent neural networks; (ii) obtaining, from each recurrent neural network from the plurality of recurrent neural networks, an intermediate growth prediction for that test mixture). Karevan discloses that the final prediction is made by a dense layer based on the hidden state output by the final LSTM layer. At 2 para.2 ((iii) obtaining, from the dense layer, a growth prediction for that test mixture based on the intermediate growth predictions for that test mixture obtained from the recurrent neural networks). Karevan teaches that LSTMs have shown significant performance on different sequence learning problems and time series prediction, and notes that considering spatio-temporal property of the data in the LSTM model can improve the performance of the prediction. At 1 para.2; 4 para.6.
Yu discloses a base AST method of imaging bacterial cells over time and analyzing the videos with a CNN deep learning algorithm to predict MIC values. Karevan discloses a LSTM model that has been improved for prediction based on spatio-temporal data by considering the property of the data. A person having ordinary skill in the art would recognize that Karevan’s spatio-temporal stacked LSTM model could be applied to Yu’s AST method to predict MIC because MIC is determined from spatio-temporal data. One of ordinary skill in the art would recognize that applying Karevan’s model would predictably yield an improved AST method to predict MIC values because considering spatio-temporal property of the data in the LSTM model can improve the performance of the prediction. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, D.
Claims 3, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yu and Karevan as applied to claims 2, 10, and 16 above, and further in view of Clinical and Laboratory Standards Institute, M100 Performance Standards for Antimicrobial Susceptibility Testing, 28th Edition (January 2018) (hereinafter “CLSI”) and Cong Duy Vu Hoang et al., Incorporating Side Information into Recurrent Neural Network Language, 2016 NAACL-HLT 1250–55 (June 2016) (hereinafter “Hoang”).
Regarding claims 3, 11, and 17, Yu and Karevan disclose the steps of claims 2, 10, and 16 (see 103 rejection above). Yu discloses that the deep learning model identifies the identity of bacterial cells in a sample before determining the minimum inhibitory concentrations (MIC). At 6315 col.1 para.2; 6319 col.2 paras.2-3. Yu notes that the current work focuses on E. coli, which necessitates validation with other pathogens and more clinical samples. At 6319 col.2 para.5. Neither Yu nor Karevan disclose wherein: (a) the machine learning model is adapted to receive an identification of a microorganism corresponding to the biological sample; and (b) the dense layer is adapted to, for each test mixture, weight the intermediate growth predictions for that test mixture from the plurality of recurrent neural networks based on the identification of the microorganism.
However, CLSI teaches that MIC values generated by a susceptibility test should be interpreted based upon established breakpoints to accurately predict clinical outcomes. At 3 para.3. CLSI demonstrates that breakpoints are specific to different bacterial species. At Table 2A – 2J. Additionally, Hoang discloses a technique for incorporating side information into RNN language models. At abstract. Hoang teaches that the auxiliary information is encoded into a vector representation and integrated into the model as part of the output layer. At 1251 col.2 paras.2-4 ((a) the machine learning model is adapted to receive an identification of a microorganism corresponding to the biological sample). Hoang discloses that the output layer weights the hidden states based on the encoded auxiliary information. At 1251 col.2 para.4 – 1252 col.1 para.1 ((b) the dense layer is adapted to, for each test mixture, weight the intermediate growth predictions for that test mixture from the plurality of recurrent neural networks based on the identification of the microorganism). Hoang demonstrates that incorporating auxiliary side information effectively boosts the performance of RNNs. At 1254 col.1 para.1.
Yu and Karevan disclose a base AST method of imaging bacterial cells over time and analyzing the videos with a spatio-temporal stacked LSTM model to predict MIC values. CLIS teaches that MIC values should be interpreted based upon established species-specific breakpoints to accurately predict clinical outcomes. Hoang discloses a technique for incorporating side information into RNN language models. A person having ordinary skill in the art would recognize that Hoang’s technique could be applied to the method of Yu and Karevan to weight the model output based on species identification because LSTM models are a type of RNN and Hoang’s technique is applicable to RNNs. One of ordinary skill in the art would recognize that applying Hoang’s technique would predictably yield an improved AST method to predict MIC values because MIC values should be interpreted based upon established species-specific breakpoints to accurately predict clinical outcomes, and incorporating auxiliary side information effectively boosts the performance of RNNs. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, D.
Claims 4, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yu and Karevan as applied to claims 2, 10, and 16 above, and further in view of Jacques Monod, The Growth of Bacterial Cultures, 3 Annual Review of Microbiology 371-94 (October 1949) (hereinafter “Monod”).
Regarding claims 4, 12 and 18, Yu and Karevan disclose the steps of claims 2, 10, and 16 (see 103 rejection above). Neither Yu nor Karevan disclose generating the temporal sequence by performing steps comprising: (a) for each imaging time from the plurality of imaging times, obtaining a doubling value by applying a log base 2 transformation to the input item corresponding to that imaging time comprised by the data sequence for that test mixture; and (b) for each doubling value except the doubling value corresponding to a first imaging time, obtaining a doubling value change by subtracting a doubling value corresponding to a preceding imaging time. However, Monod discloses that the division/growth rate of a bacterial culture is determined by applying a log base 2 transformation to the cell count at a time interval, and subtracting the log base 2 transformed cell count of the previous time interval. At 371 para.4 – 372 para.1; Equation 1. Monod notes that the use of log base 2 in place of log base 10 simplifies calculations and is especially convenient for the graphical representation of growth curves because each integer step corresponds to one cell division. At 372 footnote 1.
Yu and Karevan disclose a base AST method of imaging bacterial cells over time and analyzing the videos with a spatio-temporal stacked LSTM model to predict MIC values. Monod teaches a long-established, standard technique in MIC determination of converting bacterial count to log base 2 and examining successive changes in those values to obtain growth rates. A person having ordinary skill in the art would recognize that Monod’s growth rate calculation could be applied to the method of Yu and Karevan as a data preprocessing step because Yu extracts temporal sequences of quantitative growth measurements, which is the type of data Monod’s technique is intended for. One of ordinary skill in the art would recognize that applying Monod’s growth rate technique would predictably yield an improved AST method to predict MIC values because the use of log base 2 simplifies calculations and is convenient for the graphical representation of growth curves because each integer step corresponds to one cell division. Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, D.
Claims 5, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yu and Karevan as applied to claims 2, 10, and 16 above, and further in view of Casper Hansen, Stack machine learning models: Get better results, IBM (17 January 2020) (hereinafter “Hansen”).
Regarding claims 5, 13, and 19, Yu and Karevan disclose the steps of claims 2, 10, and 16 (see 103 rejection above). Karevan discloses using a 2-layer stacked LSTM model with 5 LSTM models in the first layer where each LSTM model corresponds to a location where data was obtained. At 2 para.2. While neither Yu nor Karevan explicitly disclose the plurality of recurrent neural network comprising 16 recurrent neural networks, the precise number 16 is an optimization decision made by routine hyperparameter experimentation. See Hansen, § Grid search before stacking. Additionally, Hansen discloses stacking of 100+ models in a single layer. § Summary. A person having ordinary skill in the art would find it obvious to tune the model of Yu and Karevan to comprise 16 RNNs because the number of individual models in a stacked model is a hyperparameter that is routinely optimized. One of ordinary skill in the art would reasonably expect success in using 16 RNNs because it is within the range disclosed by the prior art when Karevan utilizes 5 models and Hansen discloses stacking 100+ models.
Claims 6, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yu, Karevan, and Hansen as applied to claims 5, 13, and 19 above, and further in view of Shaohua Xu et al., A Parallel GRU Recurrent Network Model and its Application to Multi-Channel Time-Varying Signal Classification, 7 IEEE Access 118739-48 (5 September 2019) (hereinafter “Xu”) and Aston Zhang et al., 10.2. Gated Recurrent Units (GRU), in Dive into Deep Learning (15 November 2020) (hereinafter “Zhang”).
Regarding claims 6, 14, and 20, Yu, Karevan, and Hansen disclose the steps of claims 5, 13, and 19 (see 103 rejection above). None of the references disclose the plurality of recurrent neural networks comprising 24 gated recurrent units. However, Xu discloses stacking 12 deep gated recurrent unit (GRU) networks, each of which is composed of 6 GRU information unit layers. Xu teaches that LSTMs and GRUs are variations of RNNs. At 118740 col.1 para.2-col.2 para.1. Additionally, Zhang discloses a GRU with 32 hidden units. § 10.2.5. Concise Implementation. Zhang teaches that the number of hidden units is a hyperparameter that can be optimized. §10.2.4.1. Initializing Model Parameters. Zhang notes that GRUs offer a streamlined version of the LSTM memory cell that often achieves comparable performance but with the advantage of being faster to compute. § 10.2. Gated Recurrent Units (GRU).
A person having ordinary skill in the art would be motivated to combine the teachings of Xu and Zhang with the teachings of Yu, Karevan, and Hansen because GRUs have a faster computation time as compared to LSTMs. One of ordinary skill in the art would reasonably expect success in this combination because LSTMs and GRUs are both variations of RNNs, with GRUs offering a streamlined version of LSTMs that often achieves comparable performance. Moreover, one of ordinary skill in the art would find it obvious to use a GRU with 24 gated recurrent units because the number of hidden units is a hyperparameter that is routinely optimized. One of ordinary skill in the art would reasonably expect success in using 24 GRUs because it is within the range disclosed by the prior art when Xu utilizes 6 units and Zhang discloses using 32 units. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G.
Conclusion
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/E.A.D./ Examiner, Art Unit 1686
/OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685
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